Near-term ecological forecasting is emerging as an important tool to provide decision-support to conservation managers at time-scales ranging from days to several years, allowing for timely interventions and adaptive management. However, these forecasts are often created with a narrow-focus and spatial extent. This limits our understanding of the generality and transferability of the models and the forecasts they produce. The relatively high knowledge requirement, including the need for technical expertise in model development, data preprocessing, and performance evaluation, further limits our understanding of these forecasts. Here we explore these issues using a dataset of over 30 years of freshwater macroinvertebrate abundances, collected across more than 30 sites throughout Aotearoa New Zealand. Using these data we implemented a range of time-series models of varying complexity. Sites were randomly assigned to act as training or test data and the most recent five years of data held back as test data. We examined the accuracy and precision of each model to forecast macroinvertebrate abundance for both the training site and test sites. We used these results to derive heuristics of forecast skill degradation, identifying patterns in how prediction accuracy declines as forecast horizons increase across different systems and macroinvertebrate community traits. We also suggest a set of standardized metrics with which to compare model performance across multiple systems. Our findings provide valuable insights into the way near-term ecological forecasts may be effectively applied across freshwater ecosystems.